Federated One-Shot Ensemble Clustering

TL;DR

The paper introduces FONT, a federated clustering algorithm that enables multi-site data analysis with privacy and communication constraints, demonstrating superior performance and consistency in real-world applications.

Contribution

FONT is a novel federated clustering method that requires only one communication round and combines local models into a data-adaptive ensemble, ensuring privacy and robustness.

Findings

FONT outperforms existing benchmark methods in simulations.

It improves cluster consistency across different health systems.

Theoretical analysis supports the effectiveness of data-adaptive weights.

Abstract

Cluster analysis across multiple institutions poses significant challenges due to data-sharing restrictions. To overcome these limitations, we introduce the Federated One-shot Ensemble Clustering (FONT) algorithm, a novel solution tailored for multi-site analyses under such constraints. FONT requires only a single round of communication between sites and ensures privacy by exchanging only fitted model parameters and class labels. The algorithm combines locally fitted clustering models into a data-adaptive ensemble, making it broadly applicable to various clustering techniques and robust to differences in cluster proportions across sites. Our theoretical analysis validates the effectiveness of the data-adaptive weights learned by FONT, and simulation studies demonstrate its superior performance compared to existing benchmark methods. We applied FONT to identify subgroups of patients with rheumatoid arthritis across two health systems, revealing improved consistency of patient clusters across sites, while locally fitted clusters proved less transferable. FONT is particularly well-suited for real-world applications with stringent communication and privacy constraints, offering a scalable and practical solution for multi-site clustering.